NeuroHealth guardian: A novel hybrid approach for precision brain stroke prediction and healthcare analytics

• Published in: Journal of neuroscience methods 2024-09, Vol.409, p.110210, Article 110210
• Main Authors: Islam, Umar, Mehmood, Gulzar, Al-Atawi, Abdullah A., Khan, Faheem, Alwageed, Hathal Salamah, Cascone, Lucia
• Format: Article
• Language: English
• Subjects: Aged; Algorithms; Bayes Theorem; Brain attack; Female; Humans; K-nearest neighbor algorithm; Machine Learning; Male; Middle Aged; Stroke - physiopathology; Support Vector Machine
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2024.110210

Stroke is a severe illness, that requires early stroke detection and intervention, as this would help prevent the worsening of the condition. The research is done to solve stroke prediction problem, which may be divided into a number of sub-problems such as an individual's predisposition to develop stroke. To attain this objective, a multiturn dataset consisting of various health features, such as age, gender, hypertension, and glucose levels, takes a central role. A multiple approach was put forward concentrating on integrating the machine learning techniques, such as Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine (SV), together to develop an ensemble machine called Neuro-Health Guardian. The hypothesis "Neuro-Health Guardian Model" integrates these algorithms into one, purported to make stroke prediction more accurate. The topic dives into each instance of preparation of data for analysis, data visualization techniques, selection of the right model, training, testing, ensembling, evaluation, and prediction. The models are validated with error rate accounted from their accuracy, precision, recall, F1 score, and finally confusion matrices for a look. The study's result is showing that the ensemble model that combines the multiple algorithms has the edge over them and this is evidently by the fact that it can predict stroke rises. Additionally, accuracy, precision, recall, and F1 scores are measured in all models and the comparison is done to provide a clear comparison of the models' performance. In short, the article presented the formation of the ongoing stroke prediction that revealed the ensemble model as a good anticipation. Precise stroke predisposition forecasting can assist in early intervention thereby preventing stroke-related deaths, and limiting disability burden by stroke. The conclusions that have come out of this study offer a great action item for the development of predictive models related to stroke prevention and treatment. •This data collection helps in leading the path to the stroke risk.•To achieve this objective, a collective learning model is used.•The method focus on the most important factors, like less time in decision making and higher precision.